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Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

With increasingly stringent regulations mandating the improvement of vehicle fuel economy, automotive manufacturers face growing pressure to develop and implement technologies that improve overall system efficiency. One such technology is an automatic (auto) stop-start feature. Auto stop-start reduces idle time and reduces fuel use by temporarily shutting the engine off when the vehicle comes to a stop and automatically re-starting it when the brake is released, or the accelerator is pressed. As mandated by the U.S. Congress, the U.S. Environmental Protection Agency (EPA) is required to keep the public informed about fuel saving practices. This is done, in partnership with the U.S. Department of Energy (DOE), through the fueleconomy.gov website. The “Fuel-Saving Technologies” and “Gas Mileage Tips” sections of the website are focused on helping the public make informed purchasing decisions and encouraging fuel-saving driving habits.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Injury Risk Curves are developed from cadaver data for sternal deflections produced by anterior, distributed chest loads for a 25, 45, 55, 65 and 75 year-old Small Female, Mid-Size Male and Large Male based on the variations of bone strengths with age. These curves show that the risk of AIS ≥ 3 thoracic injury increases with the age of the person. This observation is consistent with NASS data of frontal accidents which shows that older unbelted drivers have a higher risk of AIS ≥ 3 chest injury than younger drivers.

The human thermal comfort, which has been a subject of extensive research, is a principal objective of the automotive climate control system. Applying the results of research studies to the practical problems require quantitative information of the thermal environment in the passenger compartment of a vehicle. The exposure to solar radiation is known to alter the thermal environment in the passenger compartment. A photovoltaic-cell based sensor is commonly used in the automotive climate control system to measure the solar radiation exposure of the passenger compartment of a vehicle. The erroneous information from a sensor however can cause thermal discomfort to the occupants. The erroneous measurement can be due to physical or environmental parameters. Shading of a solar sensor due to the opaque vehicle body elements is one such environmental parameter that is known to give incorrect measurement.

The vehicle air-conditioning system has significant impact on fuel economy and range of electric vehicles. Improving the fuel economy of vehicles therefore demand for energy efficient climate control systems. Also the emissions regulations motivate the reduced use of fuel for vehicle's cabin climate control. Solar heat gain of the passenger compartment by greenhouse effect is generally treated as the peak thermal load of the climate control system. Although the use of advanced glazing is considered first to reduce solar heat gain other means such as ventilation of parked car and recirculation of cabin air also have impetus for reducing the climate control loads.

In addition to the thermal comfort of the vehicle occupants, their safety by ensuring adequate visibility is an objective of the automotive climate control system. An integrated dew point and glass temperature sensor is widely used among several other technologies to detect risk of fog formation on the cabin side (or inner) surface of the windshield. The erroneous information from a sensor such as the measurement lag can cause imperfect visibility due to the delayed response of the climate control system. Also the high value, low cost vehicles may not have this sensor due to its high cost. A differential equation based model of the cabin air humidity is proposed to calculate in real-time specific humidity of the passenger compartment air. The specific humidity is used along with the windshield surface temperature to determine relative humidity of air and therefore, the risk of fog formation on the interior surface of a windshield.

This paper utilizes GPS tracked multiday travel activities to estimate the temporal distribution of electricity loads and assess battery electric vehicle (BEV) grid impacts at a significant market penetration level. The BEV load and non-PEV load vary by time of the day and day of the week. We consider two charging preferences: home priority assumes BEV drivers prefer charging at home and would not charge at public charging stations unless the state of charge (SOC) of the battery is not sufficient to cover the way back to home; and charging priority does not require drivers to defer charging to home and assumes drivers will utilize the first available charging opportunity. Both home and charging priority scenarios show an evening peak demand. Charging priority scenario also shows a morning peak on weekdays, possibly due to workplace charging.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

It is rare for an attempt towards optimization at the fleet-level when consideration is given to the sheer number of seemingly unpredictable interactions among vehicles and infrastructure in congested urban areas. To close the gap, we introduce a simulation based framework to explore the impact of speed synchronization on fuel economy improvement for fleets in traffic. The framework consists of traffic and vehicle modules. The traffic module is used to simulate driver behavior in urban traffic; and the vehicle module is employed to estimate fuel economy. Driving schedule is the linkage between these two modules. To explore the impact, a connected vehicle technology sharing vehicle speed information is used for better fuel economy of a fleet including six vehicles. In all scenarios analyzed, the leading vehicle operates under the EPA Urban Dynamometer Driving Schedule (UDDS), while the other five vehicles follow the leader consecutively.

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle.

To make informed decisions about travel and vehicle purchase, consumers need unbiased and accurate information of the fuel economy they will actually obtain. In the past, the EPA fuel economy estimates based on its 1984 rules have been widely criticized for overestimating on-road fuel economy. In 2008, EPA adopted a new estimation rule. This study compares the usefulness of the EPA's 1984 and 2008 estimates based on their prediction bias and accuracy and attempts to improve the prediction of on-road fuel economies based on consumer and vehicle attributes. We examine the usefulness of the EPA fuel economy estimates using a large sample of self-reported on-road fuel economy data and develop an Individualized Model for more accurately predicting an individual driver's on-road fuel economy based on easily determined vehicle and driver attributes. Accuracy rather than bias appears to have limited the usefulness of the EPA 1984 estimates in predicting on-road MPG.

By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.

This paper demonstrates the benefit of using simulation and robust optimization for the problem of balancing vehicle noise, vibration, and ride performance over road impacts. The psychophysics associated with perception of vehicle performance on an impact is complex because the occupants encounter both tactile and audible stimuli. Tactile impact vibration has multiple dimensions, such as impact hardness and memory shake. Audible impact sound also affects occupant perception of the vehicle quality. This paper uses multiple approaches to produce the similar, robust, optimized tuning strategies for impact performance. A Design for Six Sigma (DFSS) project was established to help identify a balanced, optimized solution. The CAE simulations were combined with software tools such as iSIGHT and internally developed Kriging software to identify response surfaces and find optimal tuning.

This paper describes the methodology used to design and perform a CFD analysis of a Chevrolet Impala SS Funny Car body. This body was designed for the purpose of making it available for teams to race it in the National Hot Rod Association (NHRA) drag racing series beginning with the 2007 race season. Several challenges were presented in this project: (1) This was the first time a General Motors drag racing body for use in professional classes (Funny Car or otherwise) was ever designed in CAD. (2) The body was originally designed as a 2007 Chevrolet Monte Carlo. After the tooling was completed, changes in Chevrolet’s product lineup required that the body be changed to a 2007 Impala SS. (3) Budget constraints precluded CFD analysis until after the bodies were already being manufactured. There were several teams that raced the new body during the 2007 race season. One of these teams won the Funny Car Driver’s Championship.

The market growth for electric-hybrid passenger vehicles has been very significant and is expected to reach nearly 25% of all vehicles sold in the US by 2015. Hybrid commercial vehicles are also being developed by several OEM's. This paper discusses the progress of Delphi Thermal Systems in developing an integrated electric compressor drive with high cooling capacity (9 kW+), sufficient for large hybrid SUV's and commercial vehicles such as Class 8 tractors with sleeper. An important driver for use of the electric compressor in the hybrid truck application is the reduction of engine idling time while maintaining comfort in the cab or sleeper. Design details of a compact 5 kW SPM motor, its inverter drive, and issues related to its integration into the compressor housing are described. Test results are given confirming excellent performance.

A unified approach to collision warning due to in-lane and neighboring traffic is presented. It is based on the concept of velocity obstacles, and is designed to alert the driver of a potential front collision and against attempting a dangerous lane change maneuver. The velocity obstacle represents the set of the host velocities that would result in collision with the respective static or moving vehicle. Potential collisions are simply determined when the velocity vector of the host vehicle penetrates the velocity obstacle of a neighboring vehicle. The generality of the velocity obstacle and its simplicity make it an attractive alternative to competing warning algorithms, and a powerful tool for generating collision avoidance maneuvers. The velocity obstacle-based warning algorithm was successfully tested in simulations using real sensor data collected during the Automotive Collision Avoidance System Field Operational Test (ACAS FOT) [10].

This paper presents the new techniques/methods being used for the rapid vehicle development and system level performance assessment. It consists of two parts: the first part presents the automated multilevel substructuring (AMLS) technique, which greatly reduces the computational demands of larger finite element models with millions of degrees of freedom(DOF) and extends the capabilities to higher frequencies and higher level of accuracy; the second part is on the superelement in conjunction with the Component Mode Synthesis (CMS) and also Automated Component Mode Synthesis (ACMS) techniques. In superelement, a full vehicle model is divided into components such as Body-in-white, Front cradle/chassis, Rear cradle/chassis, Exhaust, Engine, Transmission, Driveline, Front suspension, Rear suspension, Brake, Seats, Instrument panel, Steering system, tires, etc. with each piece represented by reduced stiffness, mass, and damping matrices.

This paper presents the hybrid technique application in identifying the noise transfer paths and the force transmissibility between the interfaces of the different components in the vehicle. It is the stiffness based formulation and is being applied for the low to mid frequency range for the vibration and structure borne noise. The frequency response functions such as dynamic compliance, mobility, inertance, and acoustic sensitivity, employed in the hybrid method, can either be from the test data or finite element solution or both. The Source-Path-Receiver concept is used. The sources can be from the road surface, engine, transmission, transfer case, prop-shaft, differential, rotating components, chain drives, pumps, etc., and the receiver can be driver/passenger ears, steering column, seats, etc.

The main objective of this paper was to determine if the vehicle performance can be maintained with a reduced mass cradle structure. Aluminum and magnesium cradles were compared with the baseline steel cradle. First, the steel chassis alone is analyzed with the refined finite element model and validated with experimental test data for the frequencies, normal modes, stiffnesses and the drive-point mobilities at various attachment mount/bushing locations. The superelement method in conjunction with the component mode synthesis (CMS) technique was used for each component of the vehicle such as Body-In-White, Instrument Panel, Steering Column Housing & Wheel, Seats, Cradles, CRFM, etc. After assemblage of all the superelements, analysis was carried out by changing the front and rear cradle gauges and the material properties. The drive-point mobility response was computed at various locations and the noise (sound pressure) level was calculated at the driver and passenger ears.